Introduction to The Tidyverse for data analysis
Julen Astigarraga
15/06/2022
Illustrations from Allison Horst
In this R Markdown file you will find the basic concepts to understand the core tidyverse collection of R packages (https://www.tidyverse.org/)
Problem we are going to work with
In the city council of Sevilla there are still discrepancies about the existence of anthropogenic climate change. Some parties are of the opinion that it does not exist, others think that it exists but that it is not due to anthropogenic causes and others argue that all the scientific evidence leaves no doubt that it exists and that it is due to anthropogenic causes. The parties that believe that anthropogenic climate change exists have asked you, a group of experts, to produce a report that clearly shows how the increase in CO2 emissions has gone hand in hand with the increase in temperature in recent decades. How would you solve this issue?
The data could be obtained, for example, from the following information sources: Climate data for Sevilla: https://verughub.github.io/easyclimate/index.html
CO2 emissions data for Spain: https://edgar.jrc.ec.europa.eu/report_2020#data_download
Wickham & Grolemund (2017)
Download data
coords <- data.frame(
lon = -5.99629,
lat = 37.3826
)
ggplot() +
borders(regions = c("Spain", "Portugal", "France")) +
geom_point(data = coords, aes(x = lon, y = lat)) +
coord_fixed(xlim = c(-10, 2), ylim = c(36, 44), ratio = 1.3) +
xlab("Longitude") +
ylab("Latitude") +
theme_bw()
# tmin <- get_daily_climate(
# coords,
# period = 1950:2020,
# climatic_var = "Tmin"
# )
#
# tmax <- get_daily_climate(
# coords,
# period = 1950:2020,
# climatic_var = "Tmax"
# )
# write_rds(tmin, file = here("00-raw", "tmin_sevilla.rds"))
# write_rds(tmax, file = here("00-raw", "tmax_sevilla.rds"))The Tidyverse
The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. — tidyverse
The tidyverse is fundamentally human centred (Wickham et al. JOSS, 2019) —
📝 few advantages compared to base R
1| readr (read rectangular data)
- 📝 Use a consistent naming scheme for the parameters (e.g. col_names and col_types not header and colClasses)
- Are much faster (up to 10x)
- Read CO2 and temperature data
co_data <- read_csv(here("02-data", "co2.csv"))
tmin_data <- read_rds(file = here("02-data", "tmin_sevilla.rds"))
tmax_data <- read_rds(file = here("02-data", "tmax_sevilla.rds"))glimpse(co_data)## Rows: 1,036
## Columns: 52
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Buil…
## $ country_name <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Anguilla"…
## $ `1970` <chr> "0.58", "0.99", "1.81", "0.12", "0.00", "0.06", "13.19", …
## $ `1971` <chr> "0.58", "0.99", "1.81", "0.12", "0.00", "0.06", "13.19", …
## $ `1972` <chr> "0.46", "1.10", "2.11", "0.14", "0.00", "0.08", "12.53", …
## $ `1973` <chr> "0.57", "1.30", "2.51", "0.14", "0.00", "0.08", "15.03", …
## $ `1974` <chr> "0.77", "1.41", "2.68", "0.13", "0.00", "0.06", "15.48", …
## $ `1975` <chr> "0.59", "1.75", "2.97", "0.12", "0.00", "0.08", "16.22", …
## $ `1976` <chr> "0.48", "1.91", "3.79", "0.09", "0.00", "0.06", "17.57", …
## $ `1977` <chr> "0.43", "2.02", "3.80", "0.10", "0.00", "0.07", "18.60", …
## $ `1978` <chr> "0.41", "2.21", "4.46", "0.33", "0.00", "0.06", "18.27", …
## $ `1979` <chr> "0.48", "2.69", "5.16", "0.56", "0.00", "0.06", "18.80", …
## $ `1980` <chr> "0.45", "1.73", "5.37", "0.70", "0.00", "0.06", "18.40", …
## $ `1981` <chr> "0.52", "1.81", "5.84", "0.66", "0.00", "0.06", "19.40", …
## $ `1982` <chr> "0.60", "1.90", "6.24", "0.38", "0.00", "0.07", "18.64", …
## $ `1983` <chr> "0.94", "2.01", "7.04", "0.50", "0.00", "0.04", "20.14", …
## $ `1984` <chr> "0.99", "2.06", "7.76", "0.55", "0.00", "0.06", "20.54", …
## $ `1985` <chr> "1.28", "2.09", "4.87", "0.60", "0.00", "0.06", "20.83", …
## $ `1986` <chr> "1.20", "2.20", "5.60", "0.62", "0.00", "0.07", "20.21", …
## $ `1987` <chr> "0.79", "2.20", "6.01", "0.63", "0.00", "0.08", "21.72", …
## $ `1988` <chr> "0.85", "2.33", "6.03", "0.75", "0.00", "0.08", "22.63", …
## $ `1989` <chr> "0.98", "1.53", "6.76", "0.73", "0.00", "0.09", "21.19", …
## $ `1990` <chr> "1.04", "2.09", "6.59", "0.66", "0.00", "0.09", "22.01", …
## $ `1991` <chr> "0.99", "1.32", "7.54", "0.69", "0.00", "0.09", "22.12", …
## $ `1992` <chr> "0.69", "0.57", "7.78", "0.62", "0.00", "0.08", "22.23", …
## $ `1993` <chr> "0.60", "0.39", "8.23", "0.51", "0.00", "0.07", "22.98", …
## $ `1994` <chr> "0.63", "0.33", "8.17", "0.63", "0.00", "0.08", "24.08", …
## $ `1995` <chr> "0.36", "0.33", "8.09", "0.67", "0.00", "0.08", "24.72", …
## $ `1996` <chr> "0.33", "0.45", "8.31", "0.91", "0.00", "0.08", "25.29", …
## $ `1997` <chr> "0.36", "0.38", "7.98", "0.91", "0.00", "0.08", "24.76", …
## $ `1998` <chr> "0.29", "0.29", "8.63", "1.01", "0.00", "0.04", "24.79", …
## $ `1999` <chr> "0.29", "0.75", "9.09", "1.26", "0.00", "0.06", "26.32", …
## $ `2000` <chr> "0.21", "0.76", "9.33", "1.05", "0.00", "0.05", "27.00", …
## $ `2001` <chr> "0.12", "0.80", "9.53", "1.17", "0.00", "0.04", "25.70", …
## $ `2002` <chr> "0.05", "1.17", "9.89", "1.20", "0.00", "0.05", "24.73", …
## $ `2003` <chr> "0.03", "1.04", "10.68", "1.26", "0.00", "0.05", "26.97",…
## $ `2004` <chr> "0.04", "0.88", "11.51", "1.30", "0.00", "0.04", "29.54",…
## $ `2005` <chr> "0.08", "0.70", "12.50", "1.00", "0.00", "0.04", "31.25",…
## $ `2006` <chr> "0.11", "0.93", "12.19", "1.96", "0.00", "0.04", "32.83",…
## $ `2007` <chr> "0.16", "0.72", "13.60", "2.33", "0.00", "0.04", "33.16",…
## $ `2008` <chr> "0.32", "0.65", "13.57", "2.97", "0.00", "0.03", "37.37",…
## $ `2009` <chr> "0.47", "0.55", "17.40", "3.51", "0.00", "0.04", "36.48",…
## $ `2010` <chr> "0.65", "0.56", "16.80", "3.77", "0.00", "0.05", "36.48",…
## $ `2011` <chr> "0.75", "0.56", "18.57", "4.15", "0.00", "0.04", "33.71",…
## $ `2012` <chr> "0.75", "0.49", "20.33", "4.61", "0.00", "0.06", "35.48",…
## $ `2013` <chr> "0.92", "0.56", "22.60", "5.69", "0.00", "0.10", "37.59",…
## $ `2014` <chr> "0.85", "0.62", "21.57", "5.61", "0.00", "0.07", "38.62",…
## $ `2015` <chr> "0.86", "0.63", "23.51", "5.06", "0.00", "0.08", "39.36",…
## $ `2016` <chr> "0.80", "0.62", "23.19", "5.03", "0.00", "0.08", "39.71",…
## $ `2017` <chr> "0.84", "0.70", "23.91", "4.14", "0.00", "0.06", "38.31",…
## $ `2018` <chr> "0.86781928", "0.70320991", "25.8825776", "4.191584", "1.…
## $ `2019` <chr> "0.89489653", "0.71658967", "27.0897153", "4.2916661", "1…head(co_data)## # A tibble: 6 × 52
## Sector country_name `1970` `1971` `1972` `1973` `1974` `1975` `1976` `1977`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Buildings Afghanistan 0.58 0.58 0.46 0.57 0.77 0.59 0.48 0.43
## 2 Buildings Albania 0.99 0.99 1.10 1.30 1.41 1.75 1.91 2.02
## 3 Buildings Algeria 1.81 1.81 2.11 2.51 2.68 2.97 3.79 3.80
## 4 Buildings Angola 0.12 0.12 0.14 0.14 0.13 0.12 0.09 0.10
## 5 Buildings Anguilla 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 6 Buildings Antigua and… 0.06 0.06 0.08 0.08 0.06 0.08 0.06 0.07
## # … with 42 more variables: `1978` <chr>, `1979` <chr>, `1980` <chr>,
## # `1981` <chr>, `1982` <chr>, `1983` <chr>, `1984` <chr>, `1985` <chr>,
## # `1986` <chr>, `1987` <chr>, `1988` <chr>, `1989` <chr>, `1990` <chr>,
## # `1991` <chr>, `1992` <chr>, `1993` <chr>, `1994` <chr>, `1995` <chr>,
## # `1996` <chr>, `1997` <chr>, `1998` <chr>, `1999` <chr>, `2000` <chr>,
## # `2001` <chr>, `2002` <chr>, `2003` <chr>, `2004` <chr>, `2005` <chr>,
## # `2006` <chr>, `2007` <chr>, `2008` <chr>, `2009` <chr>, `2010` <chr>, …tail(co_data)## # A tibble: 6 × 52
## Sector country_name `1970` `1971` `1972` `1973` `1974` `1975` `1976` `1977`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Transport Western Sah… 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02
## 2 Transport Yemen 0.66 0.66 0.92 1.25 1.25 1.02 1.53 1.47
## 3 Transport Zambia 0.63 0.63 0.69 0.73 0.69 0.74 0.77 0.68
## 4 Transport Zimbabwe 2.08 2.08 2.28 2.28 1.93 2.15 1.86 1.73
## 5 Transport Internation… 168.82 168.82 178.76 186.62 179.14 173.24 173.69 189.34
## 6 Transport Internation… 353.86 353.86 370.88 391.73 366.05 341.22 352.02 351.74
## # … with 42 more variables: `1978` <chr>, `1979` <chr>, `1980` <chr>,
## # `1981` <chr>, `1982` <chr>, `1983` <chr>, `1984` <chr>, `1985` <chr>,
## # `1986` <chr>, `1987` <chr>, `1988` <chr>, `1989` <chr>, `1990` <chr>,
## # `1991` <chr>, `1992` <chr>, `1993` <chr>, `1994` <chr>, `1995` <chr>,
## # `1996` <chr>, `1997` <chr>, `1998` <chr>, `1999` <chr>, `2000` <chr>,
## # `2001` <chr>, `2002` <chr>, `2003` <chr>, `2004` <chr>, `2005` <chr>,
## # `2006` <chr>, `2007` <chr>, `2008` <chr>, `2009` <chr>, `2010` <chr>, …summary(co_data)## Sector country_name 1970 1971
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1972 1973 1974 1975
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1976 1977 1978 1979
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1980 1981 1982 1983
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1984 1985 1986 1987
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1988 1989 1990 1991
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1992 1993 1994 1995
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 1996 1997 1998 1999
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 2000 2001 2002 2003
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 2004 2005 2006 2007
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 2008 2009 2010 2011
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 2012 2013 2014 2015
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## 2016 2017 2018 2019
## Length:1036 Length:1036 Length:1036 Length:1036
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :characternames(co_data)## [1] "Sector" "country_name" "1970" "1971" "1972"
## [6] "1973" "1974" "1975" "1976" "1977"
## [11] "1978" "1979" "1980" "1981" "1982"
## [16] "1983" "1984" "1985" "1986" "1987"
## [21] "1988" "1989" "1990" "1991" "1992"
## [26] "1993" "1994" "1995" "1996" "1997"
## [31] "1998" "1999" "2000" "2001" "2002"
## [36] "2003" "2004" "2005" "2006" "2007"
## [41] "2008" "2009" "2010" "2011" "2012"
## [46] "2013" "2014" "2015" "2016" "2017"
## [51] "2018" "2019"glimpse(tmin_data)## Rows: 25,933
## Columns: 5
## $ ID_coords <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, …
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.382…
## $ date <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "195…
## $ Tmin <dbl> 6.74, 6.46, 5.46, 3.76, 4.65, 6.57, 5.64, 6.93, 7.68, 4.71, …glimpse(tmax_data)## Rows: 25,933
## Columns: 5
## $ ID_coords <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, …
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.382…
## $ date <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "195…
## $ Tmax <dbl> 16.84, 16.99, 15.95, 18.68, 18.41, 15.73, 12.32, 16.06, 16.5…2| tibble (modern reimagining of the data.frame)
- 📝 Allow to work with list-columns
- Assists the user in displaying the printing
- Never do partial matching
class(co_data)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"co_data_df <- as.data.frame(co_data)
co_data <- as_tibble(co_data)
co_data_df$Secto## [1] "Buildings" "Buildings"
## [3] "Buildings" "Buildings"
## [5] "Buildings" "Buildings"
## [7] "Buildings" "Buildings"
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## [205] "Buildings" "Buildings"
## [207] "Other sectors" "Other sectors"
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## [383] "Other sectors" "Other sectors"
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## [387] "Other sectors" "Other sectors"
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## [405] "Other sectors" "Other sectors"
## [407] "Other sectors" "Other sectors"
## [409] "Other sectors" "Other sectors"
## [411] "Other sectors" "Other sectors"
## [413] "Other sectors" "Other sectors"
## [415] "Other industrial combustion" "Other industrial combustion"
## [417] "Other industrial combustion" "Other industrial combustion"
## [419] "Other industrial combustion" "Other industrial combustion"
## [421] "Other industrial combustion" "Other industrial combustion"
## [423] "Other industrial combustion" "Other industrial combustion"
## [425] "Other industrial combustion" "Other industrial combustion"
## [427] "Other industrial combustion" "Other industrial combustion"
## [429] "Other industrial combustion" "Other industrial combustion"
## [431] "Other industrial combustion" "Other industrial combustion"
## [433] "Other industrial combustion" "Other industrial combustion"
## [435] "Other industrial combustion" "Other industrial combustion"
## [437] "Other industrial combustion" "Other industrial combustion"
## [439] "Other industrial combustion" "Other industrial combustion"
## [441] "Other industrial combustion" "Other industrial combustion"
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## [445] "Other industrial combustion" "Other industrial combustion"
## [447] "Other industrial combustion" "Other industrial combustion"
## [449] "Other industrial combustion" "Other industrial combustion"
## [451] "Other industrial combustion" "Other industrial combustion"
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## [455] "Other industrial combustion" "Other industrial combustion"
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## [459] "Other industrial combustion" "Other industrial combustion"
## [461] "Other industrial combustion" "Other industrial combustion"
## [463] "Other industrial combustion" "Other industrial combustion"
## [465] "Other industrial combustion" "Other industrial combustion"
## [467] "Other industrial combustion" "Other industrial combustion"
## [469] "Other industrial combustion" "Other industrial combustion"
## [471] "Other industrial combustion" "Other industrial combustion"
## [473] "Other industrial combustion" "Other industrial combustion"
## [475] "Other industrial combustion" "Other industrial combustion"
## [477] "Other industrial combustion" "Other industrial combustion"
## [479] "Other industrial combustion" "Other industrial combustion"
## [481] "Other industrial combustion" "Other industrial combustion"
## [483] "Other industrial combustion" "Other industrial combustion"
## [485] "Other industrial combustion" "Other industrial combustion"
## [487] "Other industrial combustion" "Other industrial combustion"
## [489] "Other industrial combustion" "Other industrial combustion"
## [491] "Other industrial combustion" "Other industrial combustion"
## [493] "Other industrial combustion" "Other industrial combustion"
## [495] "Other industrial combustion" "Other industrial combustion"
## [497] "Other industrial combustion" "Other industrial combustion"
## [499] "Other industrial combustion" "Other industrial combustion"
## [501] "Other industrial combustion" "Other industrial combustion"
## [503] "Other industrial combustion" "Other industrial combustion"
## [505] "Other industrial combustion" "Other industrial combustion"
## [507] "Other industrial combustion" "Other industrial combustion"
## [509] "Other industrial combustion" "Other industrial combustion"
## [511] "Other industrial combustion" "Other industrial combustion"
## [513] "Other industrial combustion" "Other industrial combustion"
## [515] "Other industrial combustion" "Other industrial combustion"
## [517] "Other industrial combustion" "Other industrial combustion"
## [519] "Other industrial combustion" "Other industrial combustion"
## [521] "Other industrial combustion" "Other industrial combustion"
## [523] "Other industrial combustion" "Other industrial combustion"
## [525] "Other industrial combustion" "Other industrial combustion"
## [527] "Other industrial combustion" "Other industrial combustion"
## [529] "Other industrial combustion" "Other industrial combustion"
## [531] "Other industrial combustion" "Other industrial combustion"
## [533] "Other industrial combustion" "Other industrial combustion"
## [535] "Other industrial combustion" "Other industrial combustion"
## [537] "Other industrial combustion" "Other industrial combustion"
## [539] "Other industrial combustion" "Other industrial combustion"
## [541] "Other industrial combustion" "Other industrial combustion"
## [543] "Other industrial combustion" "Other industrial combustion"
## [545] "Other industrial combustion" "Other industrial combustion"
## [547] "Other industrial combustion" "Other industrial combustion"
## [549] "Other industrial combustion" "Other industrial combustion"
## [551] "Other industrial combustion" "Other industrial combustion"
## [553] "Other industrial combustion" "Other industrial combustion"
## [555] "Other industrial combustion" "Other industrial combustion"
## [557] "Other industrial combustion" "Other industrial combustion"
## [559] "Other industrial combustion" "Other industrial combustion"
## [561] "Other industrial combustion" "Other industrial combustion"
## [563] "Other industrial combustion" "Other industrial combustion"
## [565] "Other industrial combustion" "Other industrial combustion"
## [567] "Other industrial combustion" "Other industrial combustion"
## [569] "Other industrial combustion" "Other industrial combustion"
## [571] "Other industrial combustion" "Other industrial combustion"
## [573] "Other industrial combustion" "Other industrial combustion"
## [575] "Other industrial combustion" "Other industrial combustion"
## [577] "Other industrial combustion" "Other industrial combustion"
## [579] "Other industrial combustion" "Other industrial combustion"
## [581] "Other industrial combustion" "Other industrial combustion"
## [583] "Other industrial combustion" "Other industrial combustion"
## [585] "Other industrial combustion" "Other industrial combustion"
## [587] "Other industrial combustion" "Other industrial combustion"
## [589] "Other industrial combustion" "Other industrial combustion"
## [591] "Other industrial combustion" "Other industrial combustion"
## [593] "Other industrial combustion" "Other industrial combustion"
## [595] "Other industrial combustion" "Other industrial combustion"
## [597] "Other industrial combustion" "Other industrial combustion"
## [599] "Other industrial combustion" "Other industrial combustion"
## [601] "Other industrial combustion" "Other industrial combustion"
## [603] "Other industrial combustion" "Other industrial combustion"
## [605] "Other industrial combustion" "Other industrial combustion"
## [607] "Other industrial combustion" "Other industrial combustion"
## [609] "Other industrial combustion" "Other industrial combustion"
## [611] "Other industrial combustion" "Other industrial combustion"
## [613] "Other industrial combustion" "Other industrial combustion"
## [615] "Other industrial combustion" "Other industrial combustion"
## [617] "Other industrial combustion" "Other industrial combustion"
## [619] "Other industrial combustion" "Other industrial combustion"
## [621] "Power Industry" "Power Industry"
## [623] "Power Industry" "Power Industry"
## [625] "Power Industry" "Power Industry"
## [627] "Power Industry" "Power Industry"
## [629] "Power Industry" "Power Industry"
## [631] "Power Industry" "Power Industry"
## [633] "Power Industry" "Power Industry"
## [635] "Power Industry" "Power Industry"
## [637] "Power Industry" "Power Industry"
## [639] "Power Industry" "Power Industry"
## [641] "Power Industry" "Power Industry"
## [643] "Power Industry" "Power Industry"
## [645] "Power Industry" "Power Industry"
## [647] "Power Industry" "Power Industry"
## [649] "Power Industry" "Power Industry"
## [651] "Power Industry" "Power Industry"
## [653] "Power Industry" "Power Industry"
## [655] "Power Industry" "Power Industry"
## [657] "Power Industry" "Power Industry"
## [659] "Power Industry" "Power Industry"
## [661] "Power Industry" "Power Industry"
## [663] "Power Industry" "Power Industry"
## [665] "Power Industry" "Power Industry"
## [667] "Power Industry" "Power Industry"
## [669] "Power Industry" "Power Industry"
## [671] "Power Industry" "Power Industry"
## [673] "Power Industry" "Power Industry"
## [675] "Power Industry" "Power Industry"
## [677] "Power Industry" "Power Industry"
## [679] "Power Industry" "Power Industry"
## [681] "Power Industry" "Power Industry"
## [683] "Power Industry" "Power Industry"
## [685] "Power Industry" "Power Industry"
## [687] "Power Industry" "Power Industry"
## [689] "Power Industry" "Power Industry"
## [691] "Power Industry" "Power Industry"
## [693] "Power Industry" "Power Industry"
## [695] "Power Industry" "Power Industry"
## [697] "Power Industry" "Power Industry"
## [699] "Power Industry" "Power Industry"
## [701] "Power Industry" "Power Industry"
## [703] "Power Industry" "Power Industry"
## [705] "Power Industry" "Power Industry"
## [707] "Power Industry" "Power Industry"
## [709] "Power Industry" "Power Industry"
## [711] "Power Industry" "Power Industry"
## [713] "Power Industry" "Power Industry"
## [715] "Power Industry" "Power Industry"
## [717] "Power Industry" "Power Industry"
## [719] "Power Industry" "Power Industry"
## [721] "Power Industry" "Power Industry"
## [723] "Power Industry" "Power Industry"
## [725] "Power Industry" "Power Industry"
## [727] "Power Industry" "Power Industry"
## [729] "Power Industry" "Power Industry"
## [731] "Power Industry" "Power Industry"
## [733] "Power Industry" "Power Industry"
## [735] "Power Industry" "Power Industry"
## [737] "Power Industry" "Power Industry"
## [739] "Power Industry" "Power Industry"
## [741] "Power Industry" "Power Industry"
## [743] "Power Industry" "Power Industry"
## [745] "Power Industry" "Power Industry"
## [747] "Power Industry" "Power Industry"
## [749] "Power Industry" "Power Industry"
## [751] "Power Industry" "Power Industry"
## [753] "Power Industry" "Power Industry"
## [755] "Power Industry" "Power Industry"
## [757] "Power Industry" "Power Industry"
## [759] "Power Industry" "Power Industry"
## [761] "Power Industry" "Power Industry"
## [763] "Power Industry" "Power Industry"
## [765] "Power Industry" "Power Industry"
## [767] "Power Industry" "Power Industry"
## [769] "Power Industry" "Power Industry"
## [771] "Power Industry" "Power Industry"
## [773] "Power Industry" "Power Industry"
## [775] "Power Industry" "Power Industry"
## [777] "Power Industry" "Power Industry"
## [779] "Power Industry" "Power Industry"
## [781] "Power Industry" "Power Industry"
## [783] "Power Industry" "Power Industry"
## [785] "Power Industry" "Power Industry"
## [787] "Power Industry" "Power Industry"
## [789] "Power Industry" "Power Industry"
## [791] "Power Industry" "Power Industry"
## [793] "Power Industry" "Power Industry"
## [795] "Power Industry" "Power Industry"
## [797] "Power Industry" "Power Industry"
## [799] "Power Industry" "Power Industry"
## [801] "Power Industry" "Power Industry"
## [803] "Power Industry" "Power Industry"
## [805] "Power Industry" "Power Industry"
## [807] "Power Industry" "Power Industry"
## [809] "Power Industry" "Power Industry"
## [811] "Power Industry" "Power Industry"
## [813] "Power Industry" "Power Industry"
## [815] "Power Industry" "Power Industry"
## [817] "Power Industry" "Power Industry"
## [819] "Power Industry" "Power Industry"
## [821] "Power Industry" "Power Industry"
## [823] "Power Industry" "Power Industry"
## [825] "Power Industry" "Power Industry"
## [827] "Power Industry" "Transport"
## [829] "Transport" "Transport"
## [831] "Transport" "Transport"
## [833] "Transport" "Transport"
## [835] "Transport" "Transport"
## [837] "Transport" "Transport"
## [839] "Transport" "Transport"
## [841] "Transport" "Transport"
## [843] "Transport" "Transport"
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## [853] "Transport" "Transport"
## [855] "Transport" "Transport"
## [857] "Transport" "Transport"
## [859] "Transport" "Transport"
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## [869] "Transport" "Transport"
## [871] "Transport" "Transport"
## [873] "Transport" "Transport"
## [875] "Transport" "Transport"
## [877] "Transport" "Transport"
## [879] "Transport" "Transport"
## [881] "Transport" "Transport"
## [883] "Transport" "Transport"
## [885] "Transport" "Transport"
## [887] "Transport" "Transport"
## [889] "Transport" "Transport"
## [891] "Transport" "Transport"
## [893] "Transport" "Transport"
## [895] "Transport" "Transport"
## [897] "Transport" "Transport"
## [899] "Transport" "Transport"
## [901] "Transport" "Transport"
## [903] "Transport" "Transport"
## [905] "Transport" "Transport"
## [907] "Transport" "Transport"
## [909] "Transport" "Transport"
## [911] "Transport" "Transport"
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## [987] "Transport" "Transport"
## [989] "Transport" "Transport"
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## [1021] "Transport" "Transport"
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## [1027] "Transport" "Transport"
## [1029] "Transport" "Transport"
## [1031] "Transport" "Transport"
## [1033] "Transport" "Transport"
## [1035] "Transport" "Transport"co_data$Secto## NULL3| tidyr (tidy data)
- 📝tidy data 3 characteristics:
- Every column is a variable
- Every row is an observation
- Every cell is a single measurement
🔎 Check out this post by Julie Lowndes and Allison Horst
4. Change CO2 annual data to long format
glimpse(co_data)## Rows: 1,036
## Columns: 52
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Buil…
## $ country_name <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Anguilla"…
## $ `1970` <chr> "0.58", "0.99", "1.81", "0.12", "0.00", "0.06", "13.19", …
## $ `1971` <chr> "0.58", "0.99", "1.81", "0.12", "0.00", "0.06", "13.19", …
## $ `1972` <chr> "0.46", "1.10", "2.11", "0.14", "0.00", "0.08", "12.53", …
## $ `1973` <chr> "0.57", "1.30", "2.51", "0.14", "0.00", "0.08", "15.03", …
## $ `1974` <chr> "0.77", "1.41", "2.68", "0.13", "0.00", "0.06", "15.48", …
## $ `1975` <chr> "0.59", "1.75", "2.97", "0.12", "0.00", "0.08", "16.22", …
## $ `1976` <chr> "0.48", "1.91", "3.79", "0.09", "0.00", "0.06", "17.57", …
## $ `1977` <chr> "0.43", "2.02", "3.80", "0.10", "0.00", "0.07", "18.60", …
## $ `1978` <chr> "0.41", "2.21", "4.46", "0.33", "0.00", "0.06", "18.27", …
## $ `1979` <chr> "0.48", "2.69", "5.16", "0.56", "0.00", "0.06", "18.80", …
## $ `1980` <chr> "0.45", "1.73", "5.37", "0.70", "0.00", "0.06", "18.40", …
## $ `1981` <chr> "0.52", "1.81", "5.84", "0.66", "0.00", "0.06", "19.40", …
## $ `1982` <chr> "0.60", "1.90", "6.24", "0.38", "0.00", "0.07", "18.64", …
## $ `1983` <chr> "0.94", "2.01", "7.04", "0.50", "0.00", "0.04", "20.14", …
## $ `1984` <chr> "0.99", "2.06", "7.76", "0.55", "0.00", "0.06", "20.54", …
## $ `1985` <chr> "1.28", "2.09", "4.87", "0.60", "0.00", "0.06", "20.83", …
## $ `1986` <chr> "1.20", "2.20", "5.60", "0.62", "0.00", "0.07", "20.21", …
## $ `1987` <chr> "0.79", "2.20", "6.01", "0.63", "0.00", "0.08", "21.72", …
## $ `1988` <chr> "0.85", "2.33", "6.03", "0.75", "0.00", "0.08", "22.63", …
## $ `1989` <chr> "0.98", "1.53", "6.76", "0.73", "0.00", "0.09", "21.19", …
## $ `1990` <chr> "1.04", "2.09", "6.59", "0.66", "0.00", "0.09", "22.01", …
## $ `1991` <chr> "0.99", "1.32", "7.54", "0.69", "0.00", "0.09", "22.12", …
## $ `1992` <chr> "0.69", "0.57", "7.78", "0.62", "0.00", "0.08", "22.23", …
## $ `1993` <chr> "0.60", "0.39", "8.23", "0.51", "0.00", "0.07", "22.98", …
## $ `1994` <chr> "0.63", "0.33", "8.17", "0.63", "0.00", "0.08", "24.08", …
## $ `1995` <chr> "0.36", "0.33", "8.09", "0.67", "0.00", "0.08", "24.72", …
## $ `1996` <chr> "0.33", "0.45", "8.31", "0.91", "0.00", "0.08", "25.29", …
## $ `1997` <chr> "0.36", "0.38", "7.98", "0.91", "0.00", "0.08", "24.76", …
## $ `1998` <chr> "0.29", "0.29", "8.63", "1.01", "0.00", "0.04", "24.79", …
## $ `1999` <chr> "0.29", "0.75", "9.09", "1.26", "0.00", "0.06", "26.32", …
## $ `2000` <chr> "0.21", "0.76", "9.33", "1.05", "0.00", "0.05", "27.00", …
## $ `2001` <chr> "0.12", "0.80", "9.53", "1.17", "0.00", "0.04", "25.70", …
## $ `2002` <chr> "0.05", "1.17", "9.89", "1.20", "0.00", "0.05", "24.73", …
## $ `2003` <chr> "0.03", "1.04", "10.68", "1.26", "0.00", "0.05", "26.97",…
## $ `2004` <chr> "0.04", "0.88", "11.51", "1.30", "0.00", "0.04", "29.54",…
## $ `2005` <chr> "0.08", "0.70", "12.50", "1.00", "0.00", "0.04", "31.25",…
## $ `2006` <chr> "0.11", "0.93", "12.19", "1.96", "0.00", "0.04", "32.83",…
## $ `2007` <chr> "0.16", "0.72", "13.60", "2.33", "0.00", "0.04", "33.16",…
## $ `2008` <chr> "0.32", "0.65", "13.57", "2.97", "0.00", "0.03", "37.37",…
## $ `2009` <chr> "0.47", "0.55", "17.40", "3.51", "0.00", "0.04", "36.48",…
## $ `2010` <chr> "0.65", "0.56", "16.80", "3.77", "0.00", "0.05", "36.48",…
## $ `2011` <chr> "0.75", "0.56", "18.57", "4.15", "0.00", "0.04", "33.71",…
## $ `2012` <chr> "0.75", "0.49", "20.33", "4.61", "0.00", "0.06", "35.48",…
## $ `2013` <chr> "0.92", "0.56", "22.60", "5.69", "0.00", "0.10", "37.59",…
## $ `2014` <chr> "0.85", "0.62", "21.57", "5.61", "0.00", "0.07", "38.62",…
## $ `2015` <chr> "0.86", "0.63", "23.51", "5.06", "0.00", "0.08", "39.36",…
## $ `2016` <chr> "0.80", "0.62", "23.19", "5.03", "0.00", "0.08", "39.71",…
## $ `2017` <chr> "0.84", "0.70", "23.91", "4.14", "0.00", "0.06", "38.31",…
## $ `2018` <chr> "0.86781928", "0.70320991", "25.8825776", "4.191584", "1.…
## $ `2019` <chr> "0.89489653", "0.71658967", "27.0897153", "4.2916661", "1…co_data_l <- co_data |>
pivot_longer(
cols = "1970":"2019",
names_to = "co_year",
values_to = "co_value"
)
glimpse(co_data_l)## Rows: 51,800
## Columns: 4
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Buil…
## $ country_name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan…
## $ co_year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976", "…
## $ co_value <chr> "0.58", "0.58", "0.46", "0.57", "0.77", "0.59", "0.48", "…4| dplyr (data manipulation)
Illustrations from Allison Horst
- 📝 consistent set of verbs
- pipes (
%>%&|>)
- Filter data from Spain
- Delete (not select) those variables that you’re not interested in
glimpse(co_data_l)## Rows: 51,800
## Columns: 4
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Buil…
## $ country_name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan…
## $ co_year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976", "…
## $ co_value <chr> "0.58", "0.58", "0.46", "0.57", "0.77", "0.59", "0.48", "…co_data_l_sp <- co_data_l |>
filter(country_name == "Spain and Andorra") |>
select(!country_name)
glimpse(co_data_l_sp)## Rows: 250
## Columns: 3
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Building…
## $ co_year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976", "1977…
## $ co_value <chr> "15.20", "16.85", "17.55", "16.11", "15.63", "16.58", "16.82"…glimpse(tmin_data)## Rows: 25,933
## Columns: 5
## $ ID_coords <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, …
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.382…
## $ date <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "195…
## $ Tmin <dbl> 6.74, 6.46, 5.46, 3.76, 4.65, 6.57, 5.64, 6.93, 7.68, 4.71, …tmin_data_sel <- tmin_data |>
select(!c(ID_coords))
tmax_data_sel <- tmax_data |>
select(!c(ID_coords))
glimpse(tmin_data_sel)## Rows: 25,933
## Columns: 4
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99…
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37…
## $ date <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "1950-01-…
## $ Tmin <dbl> 6.74, 6.46, 5.46, 3.76, 4.65, 6.57, 5.64, 6.93, 7.68, 4.71, 5.01,…glimpse(tmax_data_sel)## Rows: 25,933
## Columns: 4
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99…
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37…
## $ date <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "1950-01-…
## $ Tmax <dbl> 16.84, 16.99, 15.95, 18.68, 18.41, 15.73, 12.32, 16.06, 16.51, 17…3. Bind/Join both tmin and tmax data
temp_data <- bind_cols(tmin_data_sel, tmax_data_sel)
glimpse(temp_data)## Rows: 25,933
## Columns: 8
## $ lon...1 <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -…
## $ lat...2 <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826…
## $ date...3 <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "1950…
## $ Tmin <dbl> 6.74, 6.46, 5.46, 3.76, 4.65, 6.57, 5.64, 6.93, 7.68, 4.71, 5…
## $ lon...5 <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -…
## $ lat...6 <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826…
## $ date...7 <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "1950…
## $ Tmax <dbl> 16.84, 16.99, 15.95, 18.68, 18.41, 15.73, 12.32, 16.06, 16.51…temp_data <- full_join(tmin_data_sel, tmax_data_sel,
by = c("lat", "lon", "date"))
glimpse(temp_data)## Rows: 25,933
## Columns: 5
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99…
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37…
## $ date <chr> "1950-01-01", "1950-01-02", "1950-01-03", "1950-01-04", "1950-01-…
## $ Tmin <dbl> 6.74, 6.46, 5.46, 3.76, 4.65, 6.57, 5.64, 6.93, 7.68, 4.71, 5.01,…
## $ Tmax <dbl> 16.84, 16.99, 15.95, 18.68, 18.41, 15.73, 12.32, 16.06, 16.51, 17…4. Create a new variable (column) with mutate to get mean temperature data
daily_tmean <- temp_data %>%
mutate(
Tmean = (Tmin + Tmax) / 2,
date = as.Date(date),
month = format(date, format = "%m"),
year = format(date, format = "%Y")
)5. But wait! We’re interested in annual data not daily data
glimpse(co_data_l_sp)## Rows: 250
## Columns: 3
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Building…
## $ co_year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976", "1977…
## $ co_value <chr> "15.20", "16.85", "17.55", "16.11", "15.63", "16.58", "16.82"…glimpse(daily_tmean)## Rows: 25,933
## Columns: 8
## $ lon <dbl> -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.99629, -5.9…
## $ lat <dbl> 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 37.3826, 3…
## $ date <date> 1950-01-01, 1950-01-02, 1950-01-03, 1950-01-04, 1950-01-05, 195…
## $ Tmin <dbl> 6.74, 6.46, 5.46, 3.76, 4.65, 6.57, 5.64, 6.93, 7.68, 4.71, 5.01…
## $ Tmax <dbl> 16.84, 16.99, 15.95, 18.68, 18.41, 15.73, 12.32, 16.06, 16.51, 1…
## $ Tmean <dbl> 11.790, 11.725, 10.705, 11.220, 11.530, 11.150, 8.980, 11.495, 1…
## $ month <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01"…
## $ year <chr> "1950", "1950", "1950", "1950", "1950", "1950", "1950", "1950", …annual <- daily_tmean |>
group_by(year) |>
summarise(
Tmean.year = mean(Tmean)
)
glimpse(annual)## Rows: 71
## Columns: 2
## $ year <chr> "1950", "1951", "1952", "1953", "1954", "1955", "1956", "19…
## $ Tmean.year <dbl> 18.93229, 18.15532, 17.99232, 18.84060, 18.34178, 19.62474,…6. Join both datasets and review what we have learned so far
co_temp <- full_join(
co_data_l_sp, annual, by = c("co_year" = "year")
)
glimpse(co_temp)## Rows: 271
## Columns: 4
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Buildi…
## $ co_year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976", "19…
## $ co_value <chr> "15.20", "16.85", "17.55", "16.11", "15.63", "16.58", "16.8…
## $ Tmean.year <dbl> 18.15171, 17.49244, 17.33210, 17.97434, 18.08533, 18.07388,…co_temp_all <- co_temp |>
mutate(
co_value = as.numeric(co_value),
co_year = as.numeric(co_year),
) |>
group_by(co_year) |>
summarise(
co_all = sum(co_value),
Tmean.year = first(Tmean.year)
)
glimpse(co_temp_all)## Rows: 71
## Columns: 3
## $ co_year <dbl> 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,…
## $ co_all <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Tmean.year <dbl> 18.93229, 18.15532, 17.99232, 18.84060, 18.34178, 19.62474,…5| ggplot (data visualisation)
📝 😲 ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
- Visualise the relationship between annual CO2 emissions and years
- Visualise the relationship between annual mean temperature and years
- Visualise the relationship between annual CO2 emissions and mean temperature
gg_ye_co <- ggplot(co_temp_all,
aes(x = co_year, y = co_all,
color = co_year)) +
geom_line() +
# geom_smooth(color = "#E1BE6A") +
geom_smooth(method = "lm", color = "#40B0A6")
gg_ye_temp <- ggplot(co_temp_all,
aes(x = co_year, y = Tmean.year,
color = co_year)) +
geom_point() +
# geom_smooth(color = "#E1BE6A") +
geom_smooth(method = "lm", color = "#40B0A6")
gg_co_temp <- ggplot(co_temp_all, aes(x = co_all, y = Tmean.year)) +
geom_point() +
# geom_smooth(color = "#E1BE6A") +
geom_smooth(method = "lm", color = "#40B0A6")
(gg_ye_co + gg_ye_temp) / gg_co_temp
co_temp <- co_temp |>
mutate(
across(c(co_value, co_year), ~as.numeric(.x))
)6| stringr (string manipulation)
- 📝 All functions in stringr start with str_ and take a vector of strings as the first argument
- Make working with strings as easy as possible
7| forcats (solve problems with factors)
- 📝 Solve common problems with factors
- Detect and highlight all sectors starting with the word “Other”
- Put the sectors beginning with “Other” at the end of the legend
gg_ye_co_sec <- co_temp |>
drop_na(Sector) |>
ggplot(aes(x = co_year, y = co_value, color = Sector)) +
geom_line()
co_temp_fs <- co_temp |>
mutate(
transport = str_detect(Sector, "Transport"),
Sector = fct_relevel(Sector, "Other industrial combustion", after = Inf),
Sector = fct_relevel(Sector, "Other sectors", after = Inf)
)
glimpse(co_temp_fs)## Rows: 271
## Columns: 5
## $ Sector <fct> Buildings, Buildings, Buildings, Buildings, Buildings, Buil…
## $ co_year <dbl> 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979,…
## $ co_value <dbl> 15.20, 16.85, 17.55, 16.11, 15.63, 16.58, 16.82, 17.76, 18.…
## $ Tmean.year <dbl> 18.15171, 17.49244, 17.33210, 17.97434, 18.08533, 18.07388,…
## $ transport <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…gg_ye_co_sec <- co_temp_fs |>
drop_na(Sector) |>
ggplot(aes(x = co_year, y = co_value, color = Sector)) +
geom_line() +
geom_line(data = co_temp_fs |> filter(transport == T),
aes(x = co_year, y = co_value, color = Sector),
size = 1.5)
(gg_ye_co + gg_ye_co_sec) /
(gg_ye_temp + gg_co_temp)
8| purrr (functional programming)
- 📝 The idea of passing a function to another function is an extremely powerful idea, and it’s one of the behaviours that makes R a functional programming language.
- Functional programming makes your code easier to write and to read (for loops are quite verbose, and require quite a bit of bookkeeping code that is duplicated for every for loop).
- The apply family of functions in base R solve a similar problem, but purrr is more consistent and thus is easier to learn.
Illustrations from Hadley Wickham’s talk The Joy of Functional Programming (for Data Science)
Illustrations from Hadley Wickham’s talk The Joy of Functional Programming (for Data Science)
- Create a function to add a theme to a ggplot
- Apply this function to all ggplots at once
glimpse(co_data_l)## Rows: 51,800
## Columns: 4
## $ Sector <chr> "Buildings", "Buildings", "Buildings", "Buildings", "Buil…
## $ country_name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan…
## $ co_year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976", "…
## $ co_value <chr> "0.58", "0.58", "0.46", "0.57", "0.77", "0.59", "0.48", "…co_data_l[, "country_name"]## # A tibble: 51,800 × 1
## country_name
## <chr>
## 1 Afghanistan
## 2 Afghanistan
## 3 Afghanistan
## 4 Afghanistan
## 5 Afghanistan
## 6 Afghanistan
## 7 Afghanistan
## 8 Afghanistan
## 9 Afghanistan
## 10 Afghanistan
## # … with 51,790 more rowsselect_example <- function(dat, x){
return(dat[, x])
}
select_example(co_data_l, "country_name")## # A tibble: 51,800 × 1
## country_name
## <chr>
## 1 Afghanistan
## 2 Afghanistan
## 3 Afghanistan
## 4 Afghanistan
## 5 Afghanistan
## 6 Afghanistan
## 7 Afghanistan
## 8 Afghanistan
## 9 Afghanistan
## 10 Afghanistan
## # … with 51,790 more rowsgg_theme <- function(gg, units, title){
gg +
geom_smooth(method = "lm", color = "firebrick", size = 1.3) +
stat_fit_glance(
method = "lm",
label.x = "left",
label.y = "top",
method.args = list(formula = y ~ x),
mapping = aes(
label = sprintf('italic(R^2)~"="~%.3f~~italic(P)~"="~%.2g',
after_stat(r.squared), after_stat(p.value))
),
parse = TRUE
) +
geom_point(alpha = .5) +
scale_y_continuous(labels = function(x){paste0(x, units)}) +
scale_color_viridis_c(option = "turbo", direction = -1,
name = NULL,
breaks = seq(1950, 2020, 10)) +
guides(color = guide_colorsteps(barwidth = unit(30, "lines"),
barheight = unit(.4, "lines"))) +
labs(y = NULL, x = NULL,
title = title) +
theme(
panel.grid.major = element_line(colour = "grey90", size = 0.5),
panel.background = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "top",
legend.title = element_text(size = 14, color = "grey20"),
legend.text = element_text(size = 12, color = "grey50"),
plot.title = element_text(size = 22, margin = margin(b = 15)),
plot.subtitle = element_text(size = 14, margin = margin(b = 15)),
plot.caption = element_text(size = 14, color = "grey50", margin = margin(t = 25)),
plot.title.position = "plot",
plot.caption.position = "plot",
axis.text = element_text(size = 14),
axis.line.x = element_line(color = "grey20"),
axis.ticks.x = element_line(color = "grey20"),
plot.margin = margin(20, 20, 10, 20)
)
}
gg_ye_temp_th <- gg_theme(
gg = gg_ye_temp,
units = "°C",
title = "Annual mean temperature"
)
gg_ye_co_th <- gg_theme(
gg = gg_ye_co,
units = " Mt CO2/yr",
title = bquote(Annual~CO[2]~emissions)
)
gg_co_temp_th <- gg_theme(
gg = gg_co_temp,
units = "°C",
title = bquote(
Annual~mean~temperature~vs~Annual~CO[2]~emissions
)
) +
scale_x_continuous(labels = function(x){paste0(x, " Mt CO2/yr")})
arg_3 <- list(
gg = list(gg_ye_co, gg_ye_temp, gg_co_temp),
units = c("°C", "Mt CO2/yr", "°C"),
title = c(
"Annual mean temperature",
"Annual CO2 emissions",
"Annual mean temperature vs Annual CO2 emissions"
)
)
arg_3 |>
pmap(gg_theme) |>
reduce(`+`)
gg_ye_co_sec_th <- gg_ye_co_sec +
geom_point(alpha = .5) +
scale_y_continuous(labels = function(x){paste0(x, " Mt CO2/yr")}) +
labs(y = NULL, x = NULL,
title = bquote(Annual~CO[2]~emissions~by~sectors)) +
theme(
panel.grid.major = element_line(colour = "grey90", size = 0.5),
panel.background = element_blank(),
panel.grid.minor = element_blank(),
legend.key = element_blank(),
legend.position = "top",
legend.title = element_text(size = 14, color = "grey20"),
legend.text = element_text(size = 12, color = "grey50"),
plot.title = element_text(size = 22, margin = margin(b = 15)),
plot.subtitle = element_text(size = 14, margin = margin(b = 15)),
plot.caption = element_text(size = 14, color = "grey50", margin = margin(t = 25)),
plot.title.position = "plot",
plot.caption.position = "plot",
axis.text = element_text(size = 14),
axis.line.x = element_line(color = "grey20"),
axis.ticks.x = element_line(color = "grey20"),
plot.margin = margin(20, 20, 10, 20)
)
final_plot <- (gg_ye_co_th + gg_ye_co_sec_th) /
(gg_ye_temp_th + gg_co_temp_th) +
plot_annotation(
title = expression(paste("Annual mean temperature and "~CO[2], " emissions relationship over the years in Sevilla (1950-2020)")),
caption = " Data source:
-Moreno A, Hasenauer H (2016). “Spatial downscaling of European climate data” International Journal of Climatology, 1444–1458.
-Rammer W, Pucher C, Neumann M (2018). Description, Evaluation and Validation of Downscaled Daily Climate Data Version 2.
-Cruz-Alonso V, Rodríguez-Sánchez F, Pucher C, Ratcliffe S, Astigarraga J, Neumann M, Ruiz-Benito P (2021). easyclimate: Easy access to high-resolution daily climate data for Europe.
-Crippa M, Guizzardi D, Muntean M, Schaaf E, Solazzo E, Monforti-Ferrario F, Olivier JGJ, Vignati E, Fossil CO2 emissions of all world countries - 2020 Report, EUR 30358 EN,
Publications Office of the European Union, Luxembourg, 2020.
Graphic: PhD EBD seminar",
theme = theme(
plot.title = element_text(size = 24, face = "bold"),
plot.caption = element_text(hjust = 0, size = 14, color = "grey50", margin = margin(t = 25))
)
)
ggsave(
plot = final_plot,
here("05-output", "sevilla_co2_temp.png"),
width = 20, height = 14
)3. Useful
read_all_rds <- function(path){
path %>%
dir_ls(regexp = "\\.rds$") |>
map(read_rds)
}
rds_files <- read_all_rds(path = here("02-data"))4. Advanced exercise:
- Fit a linear model for each sector
lm(Tmean.year ~ co_value, data = .x)and store it as a list-column - Predict the response for the data stored in the
datacolumn using the corresponding linear model - Calculate the correlation between the predicted response and the true response
- Run in parallel
co_temp_nested <- co_temp |>
drop_na(Sector) |>
group_by(Sector) |>
nest() |>
mutate(
lm_obj = map(data, ~lm(
Tmean.year ~ co_value, data = .x)),
pred = map2(lm_obj, data,
~predict(.x, .y)),
cor = map2_dbl(pred, data, ~cor(.x, .y$Tmean.year))
)
co_temp_nested["pred"][[1]]## [[1]]
## 1 2 3 4 5 6 7 8
## 18.17497 18.31394 18.37289 18.25162 18.21119 18.29120 18.31141 18.39058
## 9 10 11 12 13 14 15 16
## 18.46975 18.65671 18.54386 18.50343 18.57502 18.60702 18.62639 18.68366
## 17 18 19 20 21 22 23 24
## 18.69377 18.69630 18.66935 18.51522 18.61460 18.81589 18.89674 18.84284
## 25 26 27 28 29 30 31 32
## 18.94980 18.94980 19.03992 19.04076 19.03739 19.20246 19.43912 19.46944
## 33 34 35 36 37 38 39 40
## 19.51744 19.73136 20.00003 20.03287 20.08762 19.90654 19.73894 19.67999
## 41 42 43 44 45 46 47 48
## 19.87959 19.63956 19.61851 19.59830 19.43828 19.62019 19.66988 19.79453
## 49 50
## 19.80850 19.98574
##
## [[2]]
## 1 2 3 4 5 6 7 8
## 18.68744 18.73370 19.07153 18.98008 19.03764 19.08067 19.15168 19.10918
## 9 10 11 12 13 14 15 16
## 19.06131 19.14038 19.07744 19.15491 19.09304 19.09197 18.99353 18.93274
## 17 18 19 20 21 22 23 24
## 18.90638 18.91821 18.96986 19.03979 19.07529 19.07583 18.91068 18.86603
## 25 26 27 28 29 30 31 32
## 18.95748 19.03710 19.05646 19.13285 19.26626 19.32059 19.38783 19.31736
## 33 34 35 36 37 38 39 40
## 19.35986 19.45992 19.45239 19.49004 19.45938 19.47552 19.28939 18.96609
## 41 42 43 44 45 46 47 48
## 18.99621 18.95479 18.91714 18.78158 18.87841 18.88379 18.88755 18.89723
## 49 50
## 18.90567 18.91475
##
## [[3]]
## 1 2 3 4 5 6 7 8
## 19.05971 19.08279 19.07958 19.06093 19.05544 19.06316 19.05488 19.05329
## 9 10 11 12 13 14 15 16
## 19.05942 19.06138 19.06563 19.07000 19.08385 19.08024 19.08770 19.08873
## 17 18 19 20 21 22 23 24
## 19.08852 19.09144 19.08043 19.08069 19.08189 19.07642 19.08221 19.08815
## 25 26 27 28 29 30 31 32
## 19.07812 19.07597 19.08350 19.06878 19.06995 19.07759 19.06207 19.05266
## 33 34 35 36 37 38 39 40
## 19.05515 19.04096 19.03894 19.03502 19.05422 19.04353 19.05449 19.07876
## 41 42 43 44 45 46 47 48
## 19.07239 19.07955 19.07825 19.08340 19.09085 19.09608 19.09602 19.08868
## 49 50
## 19.08954 19.08398
##
## [[4]]
## 1 2 3 4 5 6 7 8
## 17.93639 17.95292 18.01403 18.10173 18.27211 18.41516 18.65119 18.45470
## 9 10 11 12 13 14 15 16
## 18.40941 18.49951 18.88651 19.09450 19.06407 19.09570 18.89753 18.76645
## 17 18 19 20 21 22 23 24
## 18.83115 18.81414 18.58266 18.96438 18.97253 18.98931 19.20976 18.97277
## 25 26 27 28 29 30 31 32
## 19.00728 19.22749 18.90831 19.20473 19.19442 19.62839 19.72807 19.56489
## 33 34 35 36 37 38 39 40
## 19.95116 19.77192 19.97129 20.18264 20.04006 20.23129 19.85388 19.48797
## 41 42 43 44 45 46 47 48
## 19.10720 19.46305 19.57423 19.04466 19.07509 19.34371 19.05017 19.31016
## 49 50
## 19.12105 18.71320
##
## [[5]]
## 1 2 3 4 5 6 7 8
## 17.96791 18.02062 18.05919 18.18521 18.25014 18.27912 18.36394 18.35124
## 9 10 11 12 13 14 15 16
## 18.40466 18.53140 18.48996 18.42862 18.42551 18.43533 18.47462 18.46168
## 17 18 19 20 21 22 23 24
## 18.49594 18.55728 18.77362 18.85484 18.92312 18.98565 19.07094 19.04698
## 25 26 27 28 29 30 31 32
## 19.10208 19.13203 19.24871 19.24703 19.42576 19.52806 19.57358 19.67133
## 33 34 35 36 37 38 39 40
## 19.71205 19.83137 19.93606 20.02614 20.11862 20.19002 20.04124 19.83568
## 41 42 43 44 45 46 47 48
## 19.76596 19.61071 19.38934 19.36370 19.37185 19.45858 19.50865 19.53045
## 49 50
## 19.57188 19.57655co_temp_nested |>
pluck("pred", 1)## 1 2 3 4 5 6 7 8
## 18.17497 18.31394 18.37289 18.25162 18.21119 18.29120 18.31141 18.39058
## 9 10 11 12 13 14 15 16
## 18.46975 18.65671 18.54386 18.50343 18.57502 18.60702 18.62639 18.68366
## 17 18 19 20 21 22 23 24
## 18.69377 18.69630 18.66935 18.51522 18.61460 18.81589 18.89674 18.84284
## 25 26 27 28 29 30 31 32
## 18.94980 18.94980 19.03992 19.04076 19.03739 19.20246 19.43912 19.46944
## 33 34 35 36 37 38 39 40
## 19.51744 19.73136 20.00003 20.03287 20.08762 19.90654 19.73894 19.67999
## 41 42 43 44 45 46 47 48
## 19.87959 19.63956 19.61851 19.59830 19.43828 19.62019 19.66988 19.79453
## 49 50
## 19.80850 19.98574co_temp_nested## # A tibble: 5 × 5
## # Groups: Sector [5]
## Sector data lm_obj pred cor
## <chr> <list> <list> <list> <dbl>
## 1 Buildings <tibble [50 × 3]> <lm> <dbl [50]> 0.763
## 2 Other sectors <tibble [50 × 3]> <lm> <dbl [50]> 0.256
## 3 Other industrial combustion <tibble [50 × 3]> <lm> <dbl [50]> 0.0201
## 4 Power Industry <tibble [50 × 3]> <lm> <dbl [50]> 0.722
## 5 Transport <tibble [50 × 3]> <lm> <dbl [50]> 0.813# parallel
plan(multisession, workers = 2)
co_temp_nested <- co_temp |>
drop_na(Sector) |>
group_by(Sector) |>
nest() |>
mutate(
lm_obj = future_map(data, ~lm(
Tmean.year ~ co_value, data = .x)),
pred = future_map2(lm_obj, data,
~predict(.x, .y)),
cor = future_map2_dbl(pred, data, ~cor(.x, .y$Tmean.year))
)Recommended reading
R for Data Science by Hadley Wickham & Garrett Grolemund (the information in this document is based mainly on this book)
Wickham et al., (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
Core tidyverse packages:
Session Info
Sys.time()## [1] "2022-06-14 10:31:49 CEST"git2r::repository()## Local: main C:/Users/julen/OneDrive/Escritorio/GitHub-col/intro_tidyverse
## Remote: main @ origin (https://github.com/Julenasti/intro_tidyverse.git)
## Head: [63cadc3] 2022-06-13: fixed sevilla plotsessionInfo()## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
## [5] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 tidyverse_1.3.1
## [9] rmarkdown_2.14 furrr_0.3.0 future_1.25.0 fs_1.5.2
## [13] ggpmisc_0.4.6 ggpp_0.4.4 ggplot2_3.3.6 viridis_0.6.2
## [17] viridisLite_0.4.0 patchwork_1.1.1 easyclimate_0.1.6 here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-157 bit64_4.0.5 lubridate_1.8.0 httr_1.4.3
## [5] rprojroot_2.0.3 tools_4.2.0 backports_1.4.1 bslib_0.3.1
## [9] utf8_1.2.2 R6_2.5.1 mgcv_1.8-40 DBI_1.1.2
## [13] colorspace_2.0-3 withr_2.5.0 tidyselect_1.1.2 gridExtra_2.3
## [17] git2r_0.30.1 bit_4.0.4 compiler_4.2.0 cli_3.3.0
## [21] rvest_1.0.2 quantreg_5.93 SparseM_1.81 xml2_1.3.3
## [25] labeling_0.4.2 sass_0.4.1 scales_1.2.0 digest_0.6.29
## [29] jpeg_0.1-9 pkgconfig_2.0.3 htmltools_0.5.2 parallelly_1.31.1
## [33] dbplyr_2.1.1 fastmap_1.1.0 highr_0.9 maps_3.4.0
## [37] rlang_1.0.2 readxl_1.4.0 rstudioapi_0.13 jquerylib_0.1.4
## [41] generics_0.1.2 farver_2.1.0 jsonlite_1.8.0 vroom_1.5.7
## [45] magrittr_2.0.3 Matrix_1.4-1 munsell_0.5.0 fansi_1.0.3
## [49] lifecycle_1.0.1 stringi_1.7.6 yaml_2.3.5 MASS_7.3-56
## [53] grid_4.2.0 parallel_4.2.0 listenv_0.8.0 crayon_1.5.1
## [57] lattice_0.20-45 haven_2.5.0 splines_4.2.0 hms_1.1.1
## [61] knitr_1.39.3 pillar_1.7.0 emo_0.0.0.9000 codetools_0.2-18
## [65] reprex_2.0.1 glue_1.6.2 evaluate_0.15 modelr_0.1.8
## [69] vctrs_0.4.1 png_0.1-7 tzdb_0.3.0 MatrixModels_0.5-0
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.31
## [77] broom_0.8.0 survival_3.3-1 globals_0.15.0 ellipsis_0.3.2